Computational metrics record the impact of the training on hardware. Particularly important on embedded devices with limited power, it measures the memory consumption —from both the models and stored samples, the number of flops, etc. For example Progressive Networks [1]} showcase strong resilience to forgetting but its memory consumption is also prohibitively high due to the cost of storing all previous models.
